Search results

This study aims to investigate the role of two structural factors – threat level depicted on fear messages and warning size – as well as two contextual factors – repeated exposure and type of packs – on pictorial and threatening tobacco warnings’ effectiveness.

A two (warning threat level: moderate vs high) × two (coverage: 40 vs 75 per cent) × two (packaging type: plain vs branded) within-subjects experiment was carried out. Subjects were exposed three times to pictorial and threatening tobacco warnings. Both self-report and psychophysiological measurements of emotion were used.

Results indicate that threat level is the most effective structural factor to influence smokers’ reactions, while warning size has very low impact. Furthermore, emotional arousal, fear and disgust, as well as attitude toward tobacco brand, decrease after the second exposure to pictorial and threatening tobacco warnings, but stay stable at the third exposure. However, there is no effect of repetition on the emotional valence component, arousal-subjective component, on intention of quitting or of reducing cigarette consumption. Finally, there is a negative effect of plain packs on attitude toward tobacco brand over repeated exposures, but there is no effect of the type of packs on smokers’ emotions and intentions.

Useful marketing social guidance, which might help government decision-makers increase the effectiveness of smoking reduction measures, is offered.

For the first time in this context, psychophysiological and self-report measurements were combined to measure smokers’ reactions toward pictorial and threatening tobacco warnings in a repeated exposure study.

Research on the role of affective forecasting in hotel service experiences is in its infancy, and several crucial questions remain unanswered. This study aims to posit that affective forecasting is a significant antecedent of customers’ affective reactions during a hotel stay. The authors investigate how customers’ service quality expectations influence their affective forecasting and how customers’ affective forecasting before an upcoming hotel service experience influences their affective reactions during the hotel service experience.

The authors collected data through online questionnaires distributed among 634 US adults who had stayed at a hotel within the past month.

The results show that: service quality expectations influence affective forecasting; affective forecasting influences affective reactions; service quality expectations influence perceived service quality, thereby influencing affective reactions and affective reactions and service quality perception influence electronic Word-Of-Mouth intentions.

The study suggests that hotel managers should identify what hotel performance attributes customers value most and depict how these attributes elicit positive affective reactions in advertising to influence customers’ purchase decisions.

This is one of the few studies to investigate the antecedents and consequences of affective forecasting in hotel service experiences.

• Connecting with customers at the organizational frontline is not only a matter of transaction but is also a matter of emotional connection

• Customers interact with retailers to seek social contact in order to recover their affective and cognitive balance

• Chatbots are well suited to resolve simple problems; they keep social interactions simple, and they provide cognitive clarity and personalized answers without engaging customers in socioaffective interactions

• Chatbots must develop the ability to read customers' emotions in order to identify the exact point at which the conversation must be managed by a human agent

Connecting with customers at the organizational frontline is not only a matter of transaction but is also a matter of emotional connection

Customers interact with retailers to seek social contact in order to recover their affective and cognitive balance

Chatbots are well suited to resolve simple problems; they keep social interactions simple, and they provide cognitive clarity and personalized answers without engaging customers in socioaffective interactions

Chatbots must develop the ability to read customers' emotions in order to identify the exact point at which the conversation must be managed by a human agent